System and method for optimizing the storage of data

ABSTRACT

The disclosure describes systems and methods for optimizing the storage of data collected from a plurality of communication channels including social data, spatial data, temporal data and logical data. Based on user data collected from past interactions with the network, clusters of objects that include common information are combined into single objects in order to save storage space and reduce the number of objects managed by the system. The combination method may also be used to dynamically identify events or conditions which are related to a plurality of entities.

BACKGROUND

A great deal of information is generated when people use electronic devices, such as when people use mobile phones and cable set-top boxes. Such information, such as location, applications used, social network, physical and online locations visited, to name a few, could be used to deliver useful services and information to end users, and provide commercial opportunities to advertisers and retailers. However, most of this information is effectively abandoned due to deficiencies in the way such information can be captured. For example, and with respect to a mobile phone, information is generally not gathered while the mobile phone is idle (i.e., not being used by a user). Other information, such as presence of others in the immediate vicinity, time and frequency of messages to other users, and activities of a user's social network are also not captured effectively.

SUMMARY

This disclosure describes systems and methods for using data collected and stored by multiple devices on a network in order to improve the performance of the services provided via the network. In particular, the disclosure describes systems and methods for identifying related communications and storing information about those communications into a single information object (IO), instead of storing a separate IO for each communication. In one application, the systems and methods can be used to dynamically identify and describe events based on information received from independent communication is over disparate communication channels by correlating information known about each communication, its sender and its recipient(s).

One aspect of the disclosure is a method for storing information objects (IOs) including generating a plurality of first IOs, each first IO containing data describing a communication transmitted via one of a plurality of communication channels, the data in each first IO identifying a user being at a location at a time. The method temporarily stores the plurality of first IOs, and subsequently compares the plurality of first IOs in order to identify a subset of first IOs containing data identifying locations within a first distance from a first location and a time within a first time period. An event IO is generated containing data identifying at least the first location, the first time period and each of the users identified in the subset of first IOs, whereby the event IO is stored and the subset of first IOs is deleted.

Another aspect of the disclosure is a method for storing information objects (IOs) which includes monitoring a plurality of communications where each communication is transmitted via one of a plurality of communication channels. A subset of related communications is identified based on an analysis of the content of the communications. The method then generates an information object (IO) containing data from each communication in the subset of related communications and stores the IO as a record of the subset of related communications.

In another aspect, the disclosure describes a computer-readable medium encoding instructions for performing a method for storing information objects (IOs). A plurality of IOs are generated containing data describing a communication transmission via one of a plurality of communication channels, wherein the data in the plurality of IOs identifies social data, spatial data, temporal data and logical data that fall within predetermined threshold. The plurality of IOs are temporarily stored whereupon they are compared to identify a subset of IOs containing related social data, spatial data, temporal data and logical data. An event IO is created based upon the subset IOs, wherein the event IO is stored on the computer-readable medium and the subset of IOs are deleted.

In yet another aspect, the disclosure describes a system for optimizing the storage of information objects (IOs) including a correlation engine connected via at least one communication channel to a plurality of computing devices transmitting the IOs over that at least one communication channel. Computer-readable media is connected to the correlation engine and at least one of social data, spatial data, temporal data and logical data associated with a plurality of real-world entities (RWEs) including the plurality of computing devices is stored. A data optimization engine, based relationships between the IOs and the RWEs determined by the correlation engine, identifies the IOs with redundant data and generates a combined IO with at least a portion of the identified IOs.

These and various other features as well as advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. Additional features are set forth in the description that follows and, in part, will be apparent from the description, or can be learned by practice of the described embodiments. The benefits and features will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the disclosure as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application, are illustrative of embodiments systems and methods described below and are not meant to limit the scope of the disclosure in any manner, which scope shall be based on the claims appended hereto.

FIG. 1 illustrates an example of the relationships between RWEs and IOs on the W4 COMN.

FIG. 2 illustrates an example of metadata defining the relationships between RWEs and IOs on the W4 COMN.

FIG. 3 illustrates a conceptual model of the W4 COMN.

FIG. 4 illustrates the functional layers of the W4 COMN architecture.

FIG. 5 illustrates an embodiment of analysis components of a W4 engine as shown in FIG. 2.

FIG. 6 illustrates an embodiment of a W4 engine showing different components within the sub-engines described generally above with reference to FIG. 5.

FIG. 7 illustrates some of the elements in a W4 engine adapted to optimize the storage of data derived from communications over disparate communication channels.

FIG. 8 illustrates an embodiment of a method for storing information objects containing data derived from communications over disparate communication channels.

DETAILED DESCRIPTION

This disclosure describes a communication network, referred herein as the “W4 Communications Network” or W4 COMN, that uses information related to the “Who, What, When and Where” of interactions with the network to provide improved services to the network's users. The W4 COMN is a collection of users, devices and processes that foster both synchronous and asynchronous communications between users and their proxies. It includes an instrumented network of sensors providing data recognition and collection in real-world environments about any subject, location, user or combination thereof.

As a communication network, the W4 COMN handles the routing/addressing, scheduling, filtering, prioritization, replying, forwarding, storing, deleting, privacy, transacting, triggering of a new message, propagating changes, transcoding and linking. Furthermore, these actions can be performed on any communication channel accessible by the W4 COMN.

The W4 COMN uses a data modeling strategy for creating profiles for not only users and locations but also any device on the network and any kind of user-defined data with user-specified conditions from a rich set of possibilities. Using Social, Spatial, Temporal and Logical data available about a specific user, topic or logical data object, every entity known to the W4 COMN can be mapped and represented against all other known entities and data objects in order to create both a micro graph for every entity as well as a global graph that interrelates all known entities against each other and their attributed relations.

In order to describe the operation of the W4 COMN, two elements upon which the W4 COMN is built must first be introduced, real-world entities and information objects. These distinction are made in order to enable correlations to be made from which relationships between electronic/logical objects and real objects can be determined. A real-world entity (RWE) refers to a person, device, location, or other physical thing known to the W4 COMN. Each RWE known to the W4 COMN is assigned or otherwise provided with a unique W4 identification number that absolutely identifies the RWE within the W4 COMN.

RWEs can interact with the network directly or through proxies, which can themselves be RWEs. Examples of RWEs that interact directly with the W4 COMN include any device such as a sensor, motor, or other piece of hardware that connects to the W4 COMN in order to receive or transmit data or control signals. Because the W4 COMN can be adapted to use any and all types of data communication, the devices that can be RWEs include all devices that can serve as network nodes or generate, request and/or consume data in a networked environment or that can be controlled via the network. Such devices include any kind of “dumb” device purpose-designed to interact with a network (e.g., cell phones, cable television set top boxes, fax machines, telephones, and radio frequency identification (RFID) tags, sensors, etc.). Typically, such devices are primarily hardware and their operations can not be considered separately from the physical device.

Examples of RWEs that must use proxies to interact with W4 COMN network include ail non-electronic entities including physical entities, such as people, locations (e.g., states, cities, houses, buildings, airports, roads, etc.) and things (e.g., animals, pets, livestock, gardens, physical objects, cars, airplanes, works of art, etc.), and intangible entities such as business entities, legal entities, groups of people or sports teams. In addition, “smart” devices (e.g., computing devices such as smart phones, smart set top boxes, smart cars that support communication with other devices or networks, laptop computers, personal computers, server computers, satellites, etc.) are also considered RWEs that must use proxies to interact with the network. Smart devices are electronic devices that can execute software via an internal processor in order to interact with a network. For smart devices, it is actually the executing software application(s) that interact with the W4 COMN and serve as the devices' proxies.

The W4 COMN allows associations between RWEs to be determined and tracked. For example, a given user (an RWE) can be associated with any number and type of other RWEs including other people, cell phones, smart credit cards, personal data assistants, email and other communication service accounts, networked computers, smart appliances, set top boxes and receivers for cable television and other media services, and any other networked device. This association can be made explicitly by the user, such as when the RWE is installed into the W4 COMN. An example of this is the set up of a new cell phone, cable television service or email account in which a user explicitly identifies an RWE (e.g., the user's phone for the cell phone service, the user's set top box and/or a location for cable service, or a username and password for the online service) as being directly associated with the user. This explicit association can include the user identifying a specific relationship between the user and the RWE (e.g., this is my device, this is my home appliance, this person is my friend/father/son/etc., this device is shared between me and other users, etc.). RWEs can also be implicitly associated with a user based on a current situation. For example, a weather sensor on the W4 COMN can be implicitly associated with a user based on information indicating that the user lives or is passing near the sensor's location.

An information object (IO), on the other hand, is a logical object that stores, maintains, generates, serves as a source for or otherwise provides data for use by RWEs and/or the W4 COMN. IOs are distinct from RWEs in that IOs represent data, whereas RWEs can create or consume data (often by creating or consuming IOs) during their interaction with the W4 COMN. Examples of IOs include passive objects such as communication signals (e.g., digital and analog telephone signals, streaming media and interprocess communications), email messages, transaction records, virtual cards, event records (e.g., a data file identifying a time, possibly in combination with one or more RWEs such as users and locations, that can further be associated with a known topic/activity/significance such as a concert, rally, meeting, sporting event, etc.), recordings of phone calls, calendar entries, web pages, database entries, electronic media objects (e.g., media files containing songs, videos, pictures, images, audio messages, phone calls, etc.), electronic files and associated metadata.

In addition, IOs include any executing process or application that consumes or generates data such as an email communication application (such as OUTLOOK by MICROSOFT, or YAHOO! MAIL by YAHOO!), a calendaring application, a word processing application, an image editing application, a media player application, a weather monitoring application, a browser application and a web page server application. Such active IOs can or can not serve as a proxy for one or more RWEs. For example, voice communication software on a smart phone can serve as the proxy for both the smart phone and for the owner of the smart phone.

An IO in the W4 COMN can be provided a unique W4 identification number that absolutely identifies the IO within the W4 COMN. Although data in an IO can be revised by the act of an RWE, the IO remains a passive, logical data representation or data source and, thus, is not an RWE.

For every IO there are at least three classes of associated RWEs. The first is the RWE who owns or controls the IO, whether as the creator or a rights holder (e.g., an RWE with editing rights or use rights to the IO). The second is the RWE(s) that the IO relates to, for example by containing information about the RWE or that identifies the RWE. The third are any RWEs who then pay any attention (directly or through a proxy process) to the IO, in which “paying attention” refers to accessing the IO in order to obtain data from the IO for some purpose.

“Available data” and “W4 data” means data that exists in an IO in some form somewhere or data that can be collected as needed from a known IO or RWE such as a deployed sensor. “Sensor” means any source of W4 data including PCs, phones, portable PCs or other wireless devices, household devices, cars, appliances, security scanners, video surveillance, RFID tags in clothes, products and locations, online data or any other source of information about a real-world user/topic/thing (RWE) or logic-based agent/process/topic/thing (IO).

FIG. 1 illustrates an example of the relationships between RWEs and IOs on the W4 COMN. In the embodiment illustrated, a user 102 is a RWE of the network provided with a unique network ID. The user 102 is a human that communicates with the network via the proxy devices 104, 106, 108, 110 associated with the user 102, all of which are RWEs of the network and provided with their own unique network ID. Some of these proxies can communicate directly with the W4 COMN or can communicate with the W4 COMN via IOs such as applications executed on or by the device.

As mentioned above the proxy devices 104, 106, 108, 110 can be explicitly associated with the user 102. For example, one device 104 can be a smart phone connected by a cellular service provider to the network and another device 106 can be a smart vehicle that is connected to the network. Other devices can be implicitly associated with the user 102. For example, one device 108 can be a “dumb” weather sensor at a location matching the current location of the user's cell phone 104, and thus implicitly associated with the user 102 while the two RWEs 104, 108 are co-located. Another implicitly associated device 110 can be a sensor 110 for physical location 112 known to the W4 COMN. The location 112 is known, either explicitly (through a user-designated relationship, e.g., this is my home, place of employment, parent, etc.) or implicitly (the user 102 is often co-located with the RWE 112 as evidenced by data from the sensor 110 at that location 112), to be associated with the first user 102.

The user 102 can also be directly associated with other people, such as the person 140 shown, and then indirectly associated with other people 142, 144 through their associations as shown. Again, such associations can be explicit (e.g., the user 102 can have identified the associated person 140 as his/her father, or can have identified the person 140 as a member of the user's social network) or implicit (e.g., they share the same address).

Tracking the associations between people (and other RWEs as well) allows the creation of the concept of “intimacy”: Intimacy being a measure of the degree of association between two people or RWEs. For example, each degree of removal between RWEs can be considered a lower level of intimacy, and assigned lower intimacy score. Intimacy can be based solely on explicit social data or can be expanded to include all W4 data including spatial data and temporal data.

Each RWE 102, 104, 106, 108, 110, 112, 140, 142, 144 of the W4 COMN can be associated with one or more IOs as shown. Continuing the examples discussed above, FIG. 1 illustrates two IOs 122, 124 as associated with the cell phone device 104. One IO 122 can be a passive data object such as an event record that is used by scheduling/calendaring software on the cell phone, a contact IO used by an address book application, a historical record of a transaction made using the device 104 or a copy of a message sent from the device 104. The other IO 124 can be an active software process or application that serves as the device's proxy to the W4 COMN by transmitting or receiving data via the W4 COMN. Voice communication software, scheduling/calendaring software, an address book application or a text messaging application are all examples of IOs that can communicate with other IOs and RWEs on the network. The IOs 122, 124 can be locally stored on the device 104 or stored remotely on some node or datastore accessible to the W4 COMN, such as a message server or cell phone service datacenter. The IO 126 associated with the vehicle 108 can be an electronic file containing the specifications and/or current status of the vehicle 108, such as make, model, identification number, current location, current speed, current condition, current owner, etc. The IO 128 associated with sensor 108 can identify the current state of the subject(s) monitored by the sensor 108, such as current weather or current traffic. The IO 130 associated with the cell phone 110 can be information in a database identifying recent calls or the amount of charges on the current bill.

Furthermore, those RWEs which can only interact with the W4 COMN through proxies, such as the people 102, 140, 142, 144, computing devices 104, 106 and location 112, can have one or more IOs 132, 134, 146, 148, 150 directly associated with them. An example includes IOs 132, 134 that contain contact and other RWE-specific information. For example, a person's IO 132, 146, 148, 150 can be a user profile containing email addresses, telephone numbers, physical addresses, user preferences, identification of devices and other RWEs associated with the user, records of the user's past interactions with other RWE's on the W4 COMN (e.g., transaction records, copies of messages, listings of time and location combinations recording the user's whereabouts in the past), the unique W4 COMN identifier for the location and/or any relationship information (e.g., explicit user-designations of the user's relationships with relatives, employers, co-workers, neighbors, service providers, etc.). Another example of a person's IO 132, 146, 148, 150 includes remote applications through which a person can communicate with the W4 COMN such as an account with a web-based email service Such as Yahoo! Mail. The location's IO 134 can contain information such as the exact coordinates of the location, driving directions to the location, a classification of the location (residence, place of business, public, non-public, etc.), information about the services or products that can be obtained at the location, the unique W4 COMN identifier for the location, businesses located at the location, photographs of the location, etc.

In order to correlate RWEs and IOs to identify relationships, the W4 COMN makes extensive use of existing metadata and generates additional metadata where necessary. Metadata is loosely defined as data that describes data. For example, given an IO such as a music file, the core, primary or object data of the music file is the actual music data that is converted by a media player into audio that is heard by the listener. Metadata for the same music file can include data identifying the artist, song, etc., album art, and the format of the music data. This metadata can be stored as part of the music file or in one or more different IOs that are associated with the music file or both. In addition, W4 metadata for the same music file can include the owner of the music file and the rights the owner has in the music file. As another example, if the IO is a picture taken by an electronic camera, the picture can include in addition to the primary image data from which an image can be created on a display, metadata identifying when the picture was taken, where the camera was when the picture was taken, what camera took the picture, who, if anyone, is associated (e.g., designated as the camera's owner) with the camera, and who and what are the subjects of/in the picture. The W4 COMN uses all the available metadata in order to identify implicit and explicit associations between entities and data objects.

FIG. 2 illustrates an example of metadata defining the relationships between RWEs and IOs on the W4 COMN. In the embodiment shown, an IO 202 includes object data 204 and five discrete items of metadata 206, 208, 210, 212, 214. Some items of metadata 208, 210, 212 can contain information related only to the object data 204 and unrelated to any other IO or RWE. For example, a creation date, text or an image that is to be associated with the object data 204 of the IO 202.

Some of items of metadata 206, 214, on the other hand, can identify relationships between the IO 202 and other RWEs and IOs. As illustrated, the IO 202 is associated by one item of metadata 206 with an RWE 220 that RWE 220 is further associated with two IOs 224, 226 and a second RWE 222 based on some information known to the W4 COMN. This part of FIG. 2, for example, could describe the relations between a picture (IO 202) containing metadata 206 that identifies the electronic camera (the first RWE 220) and the user (the second RWE 224) that is known by the system to be the owner of the camera 220. Such ownership information can be determined, for example, from one or another of the IOs 224, 226 associated with the camera 220.

FIG. 2 also illustrates metadata 214 that associates the IO 202 with another IO 230. This IO 230 is itself associated with three other IOs 232, 234, 236 that are further associated with different RWEs 242, 244, 246. This part of FIG. 2, for example, could describe the relations between a music file (IO 202) containing metadata 206 that identifies the digital rights file (the first IO 230) that defines the scope of the rights of use associated with this music file 202. The other IOs 232, 234, 236 are other music files that are associated with the rights of use and which are currently associated with specific owners (RWEs 242, 244, 246).

FIG. 3 illustrates a conceptual model of the W4 COMN. The W4 COMN 300 creates an instrumented messaging infrastructure in the form of a global logical network cloud conceptually sub-divided into networked-clouds for each of the 4Ws: Who, Where, What and When. In the Who cloud 302 are all users whether acting as senders, receivers, data points or confirmation/certification sources as well as user proxies in the forms of user-program processes, devices, agents, calendars, etc. In the Where cloud 304 are all physical locations, events, sensors or other RWEs associated with a spatial reference point or location. The When cloud 306 is composed of natural temporal events (that is events that are not associated with particular location or person such as days, times, seasons) as well as collective user temporal events (holidays, anniversaries, elections, etc.) and user-defined temporal events (birthdays, smart-timing programs). The What cloud 308 is comprised of all known data—web or private, commercial or user—accessible to the W4 COMN, including for example environmental data like weather and news, RWE-generated data, IOs and IO data, user data, models, processes and applications. Thus, conceptually, most data is contained in the What cloud 308.

As this is just a conceptual model, it should be noted that some entities, sensors or data will naturally exist in multiple clouds either disparate in time or simultaneously. Additionally, some IOs and RWEs can be composites in that they combine elements from one or more clouds. Such composites can be classified or not as appropriate to facilitate the determination of associations between RWEs and IOs. For example, an event consisting of a location and time could be equally classified within the When cloud 306, the What cloud 308 and/or the Where cloud 304.

The W4 engine 310 is center of the W4 COMN's central intelligence for making all decisions in the W4 COMN. An “engine” as referred to herein is meant to describe a software, hardware or firmware (or combinations thereof) system, process or functionality that performs or facilitates the processes, features and/or functions described herein (with or without human interaction or augmentation). The W4 engine 310 controls all interactions between each layer of the W4 COMN and is responsible for executing any approved user or application objective enabled by W4 COMN operations or interoperating applications. In an embodiment, the W4 COMN is an open platform upon which anyone can write an application. To support this, it includes standard published APIs for requesting (among other things) synchronization, disambiguation, user or topic addressing, access rights, prioritization or other value-based ranking, smart scheduling, automation and topical, social, spatial or temporal alerts.

One function of the W4 COMN is to collect data concerning all communications and interactions conducted via the W4 COMN, which can include storing copies of IOs and information identifying all RWEs and other information related to the IOs (e.g., who, what, when, where information). Other data collected by the W4 COMN can include information about the status of any given RWE and IO at any given time, such as the location, operational state, monitored conditions (e.g., for an RWE that is a weather sensor, the current weather conditions being monitored or for an RWE that is a cell phone, its current location based on the cellular towers it is in contact with) and current status.

The W4 engine 310 is also responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN. The function of identifying RWEs associated with or implicated by IOs and actions performed by other RWEs is referred to as entity extraction. Entity extraction includes both simple actions, such as identifying the sender and receivers of a particular IO, and more complicated analyses of the data collected by and/or available to the W4 COMN, for example determining that a message listed the time and location of an upcoming event and associating that event with the sender and receiver(s) of the message based on the context of the message or determining that an RWE is stuck in a traffic jam based on a correlation of the RWE's location with the status of a co-located traffic monitor.

It should be noted that when performing entity extraction from an IO, the IO can be an opaque object with only W4 metadata related to the object (e.g., date of creation, owner, recipient, transmitting and receiving RWEs, type of IO, etc.), but no knowledge of the internals of the IO (i.e., the actual primary or object data contained within the object). Knowing the content of the IO does not prevent W4 data about the IO (or RWE) to be gathered. The content of the IO if known can also be used in entity extraction, if available, but regardless of the data available entity extraction is performed by the network based on the available data, Likewise, W4 data extracted around the object can be used to imply attributes about the object itself, while in other embodiments, full access to the IO is possible and RWEs can thus also be extracted by analyzing the content of the object, e.g. strings within an email are extracted and associated as RWEs to for use in determining the relationships between the sender, user, topic or other RWE or IO impacted by the object or process.

In an embodiment, the W4 engine 310 represents a group of applications executing on one or more computing devices that are nodes of the W4 COMN. For the purposes of this disclosure, a computing device is a device that includes a processor and memory for storing data and executing software (e.g., applications) that perform the functions described. Computing devices can be provided with operating systems that allow the execution of software applications in order to manipulate data.

In the embodiment shown, the W4 engine 310 can be one or a group of distributed computing devices, such as a general-purpose personal computers (PCs) or purpose built server computers, connected to the W4 COMN by suitable communication hardware and/or software. Such computing devices can be a single device or a group of devices acting together. Computing devices can be provided with any number of program modules and data files stored in a local or remote mass storage device and local memory (e.g., RAM) of the computing device. For example, as mentioned above, a computing device can include an operating system suitable for controlling the operation of a networked computer, such as the WINDOWS XP or WINDOWS SERVER operating systems from MICROSOFT CORPORATION.

Some RWEs can also be computing devices such as smart phones, web-enabled appliances, PCs, laptop computers, and personal data assistants (PDAs). Computing devices can be connected to one or more communications networks such as the Internet, a publicly switched telephone network, a cellular telephone network, a satellite communication network, a wired communication network such as a cable television or private area network. Computing devices can be connected any such network via a wired data connection or wireless connection such as a wi-fi, a WiMAX (802.36), a Bluetooth or a cellular telephone connection.

Local data structures, including discrete IOs, can be stored on a mass storage device (not shown) that is connected to, or part of, any of the computing devices described herein including the W4 engine 310. For example, in an embodiment, the data backbone of the W4 COMN, discussed below, includes multiple mass storage devices that maintain the IOs, metadata and data necessary to determine relationships between RWEs and IOs as described herein. A mass storage device includes some form of computer-readable media and provides non-volatile storage of data and software for retrieval and later use by one or more computing devices. Although the description of computer-readable media contained herein refers to a mass storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed by a computing device.

By way of example, and not limitation, computer-readable media can comprise computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

FIG. 4 illustrates the functional layers of the W4 COMN architecture. At the lowest layer, referred to as the sensor layer 402, is the network 404 of the actual devices, users, nodes and other RWEs. The instrumentation of the network nodes to utilize them as sensors include known technologies like web analytics, GPS, cell-tower pings, use logs, credit card transactions, online purchases, explicit user profiles and implicit user profiling achieved through behavioral targeting, search analysis and other analytics models used to optimize specific network applications or functions.

The next layer is the data layer 406 in which the data produced by the sensor layer 402 is stored and cataloged. The data can be managed by either the network 404 of sensors or the network infrastructure 406 that is built on top of the instrumented network of users, devices, agents, locations, processes and sensors. The network infrastructure 408 is the core under-the-covers network infrastructure that includes the hardware and software necessary to receive that transmit data from the sensors, devices, etc. of the network 404. It further includes the processing and storage capability necessary to meaningfully categorize and track the data created by the network 404.

The next layer of the W4 COMN is the user profiling layer 410. This layer 410 can further be distributed between the network infrastructure 408 and user applications/processes 412 executing on the W4 engine or disparate user computing devices. The user profiling layer 410 performs the W4 COMN's user profiling functions. Personalization is enabled across any single or combination of communication channels and modes including email, IM, texting (SMS, etc.), photobloging, audio (e.g. telephone call), video (teleconferencing, live broadcast), games, data confidence processes, security, certification or any other W4 COMM process call for available data.

In one embodiment, the user profiling layer 410 is a logic-based layer above all sensors to which sensor data are sent in the rawest form to be mapped and placed into the W4 COMN data backbone 420. The data (collected and refined, related and deduplicated, synchronized and disambiguated) are then stored in one or a collection of related databases available to all processes of all applications approved on the W4 COMN. All Network-originating actions and communications are based upon the fields of the data backbone, and some of these actions are such that they themselves become records somewhere in the backbone, e.g. invoicing, while others, e.g. fraud detection, synchronization, disambiguation, can be done without an impact to profiles and models within the backbone.

Actions originating from anything other than the network, e.g., RWEs such as users, locations, proxies and processes, come from the applications layer 414 of the W4 COMN. Some applications can be developed by the W4 COMN operator and appear to be implemented as part of the communications infrastructure 408, e.g. email or calendar programs because of how closely they operate with the sensor processing and user profiling layer 410. The applications 412 also serve some role as a sensor in that they, through their actions, generate data back to the data layer 406 via the data backbone concerning any data created or available due to the applications execution.

The applications layer 414 also provides a personalized user interface (UI) based upon device, network, carrier as well as user-selected or security-based customizations. Any UI can operate within the W4 COMN if it is instrumented to provide data on user interactions or actions back to the network. This is a basic sensor function of any W4 COMN application/UI, and although the W4 COMN can interoperate with applications/UIs that are not instrumented, it is only in a delivery capacity and those applications/UIs would not be able to provide any data (let alone the rich data otherwise available from W4-enabled devices,)

In the case of W4 COMN mobile devices, the UI can also be used to confirm or disambiguate incomplete W4 data in real-time, as well as correlation, triangulation and synchronization sensors for other nearby enabled or non-enabled devices. At some point, the network effects of enough enabled devices allow the network to gather complete or nearly complete data (sufficient for profiling and tracking) of a non-enabled device because of its regular intersection and sensing by enabled devices in its real-world location.

Above the applications layer 414 (and sometimes hosted within it) is the communications delivery network(s) 416. This can be operated by the W4 COMN operator or be independent third-party carrier service, but in either case it functions to deliver the data via synchronous or asynchronous communication. In every case, the communication delivery network 414 will be sending or receiving data (e.g., http or IP packets) on behalf of a specific application or network infrastructure 408 request.

The communication delivery layer 418 also has elements that act as sensors including W4 entity extraction from phone calls, emails, blogs, etc. as well as specific user commands within the delivery network context, e.g., “save and prioritize this call” said before end of call can trigger a recording of the previous conversation to be saved and for the W4 entities within the conversation to analyzed and increased in weighting prioritization decisions in the personalization/user profiling layer 410.

FIG. 5 illustrates an embodiment of analysis components of a W4 engine as shown in FIG. 3. As discussed above, the W4 Engine is responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN.

In one embodiment the W4 engine connects, interoperates and instruments all network participants through a series of sub-engines that perform different operations in the entity extraction process. One such sub-engine is an attribution engine 504. The attribution engine 504 tracks the real-world ownership, control, publishing or other conditional rights of any RWE in any IO. Whenever a new IO is detected by the W4 engine 502, e.g., through creation or transmission of a new message, a new transaction record, a new image file, etc., ownership is assigned to the IO. The attribution engine 504 creates this ownership information and further allows this information to be determined for each IO known to the W4 COMN.

The W4 engine 502 further includes a correlation engine 506. The correlation engine 506 operates in two capacities: first, to identify associated RWEs and IOs and their relationships (such as by creating a combined graph of any combination of RWEs and IOs and their attributes, relationships and reputations within contexts or situations) and second, as a sensor analytics pre-processor for attention events from any internal or external source.

In one embodiment, the identification of associated RWEs and IOs function of the correlation engine 506 is done by graphing the available data. In this embodiment, a histogram of all RWEs and IOs is created, from which correlations based on the graph can be made. Graphing, or the act of creating a histogram, is a computer science method of identifying a distribution of data in order to identify relevant information and make correlations between the data. In a more general mathematical sense, a histogram is simply a mapping m_(i) that counts the number of observations that fall into various disjoint categories (known as bins), whereas the graph of a histogram is merely one way to represent a histogram. By selecting each IO, RWE, and other known parameters (e.g., times, dates, locations, etc.) as different bins and mapping the available data, relationships between RWEs, IOs and the other parameters can be identified.

As a pre-processor, the correlation engine 506 monitors the information provided by RWEs in order to determine if any conditions are identified that can trigger an action on the part of the W4 engine 502. For example, if a delivery condition has been associated with a message, when the correlation engine 506 determines that the condition is met, it can transmit the appropriate trigger information to the W4 engine 502 that triggers delivery of the message.

The attention engine 508 instruments all appropriate network nodes, clouds, users, applications or any combination thereof and includes close interaction with both the correlation engine 506 and the attribution engine 504.

FIG. 6 illustrates an embodiment of a W4 engine showing different components within the sub-engines described generally above with reference to FIG. 4. In one embodiment the W4 engine 602 includes an attention engine 608, attribution engine 604 and correlation engine 606 with several sub-managers based upon basic function.

The attention engine 608 includes a message intake and generation manager 610 as well as a message delivery manager 612 that work closely with both a message matching manager 614 and a real-time communications manager 616 to deliver and instrument all communications across the W4 COMN.

The attribution engine 604 works within tile user profile manager 618 and in conjunction with all other modules to identify, process/verify and represent ownership and rights information related to RWEs, IOs and combinations thereof.

The correlation engine 606 dumps data from both of its channels (sensors and processes) into the same data backbone 620 which is organized and controlled by the W4 analytics manager 622 and includes both aggregated and individualized archived versions of data from all network operations including user logs 624, attention rank place logs 626, web indices and environmental logs 618, e-commerce and financial transaction information 630, search indexes and logs 632, sponsor content or conditionals, ad copy and any and all other data used in any W4COMN process, IO or event. Because of the amount of data that the W4 COMN will potentially store, the data backbone 620 includes numerous database servers and datastores in communication with the W4 COMN to provide sufficient storage capacity.

As discussed above, the data collected by the W4 COMN includes spatial data, temporal data, RWE interaction data, IO content data (e.g., media data), and user data including explicitly-provided and deduced social and relationship data. Spatial data can be any data identifying a location associated with an RWE. For example, the spatial data can include any passively collected location data, such as cell tower data, global packet radio service (GPRS) data, global positioning service (GPS) data, WI-FI data, personal area network data, IP address data and data from other network access points, or actively collected location data, such as location data entered by the user.

Temporal data is time based data (e.g., time stamps) that relate to specific times and/or events associated with a user and/or the electronic device. For example, the temporal data can be passively collected time data (e.g., time data from a clock resident on the electronic device, or time data from a network clock), or the temporal data can be actively collected time data, such as time data entered by the user of the electronic device (e.g., a user maintained calendar).

The interaction data can be any data associated with user interaction of the electronic device, whether active or passive. Examples of interaction data include interpersonal communication data, media data, relationship data, transactional data and device interaction data, all of which are described in further detail below. Table 1, below, is a non-exhaustive list including examples of electronic data.

TABLE 1 Examples of Electronic Data Spatial Data Temporal Data Interaction Data Cell tower data Time stamps Interpersonal GPRS data Local clock communication data GPS data Network clock Media data WiFi data User input of Relationship data Personal area network data time data Transactional data Network access points data Device interaction data User input of location data Geo-coordinates data

With respect to the interaction data, communications between any RWEs can generate communication data that is transferred via the W4 COMN. For example, the communication data can be any data associated with an incoming or outgoing short message service (SMS) message, email message, voice call (e.g., a cell phone call, a voice over IP call), or other type of interpersonal communication relative to an RWE, such as information regarding who is sending and receiving the communication(s). As described above, communication data can be correlated with, for example, temporal data to deduce information regarding frequency of communications, including concentrated communication patterns, which can indicate user activity information.

Logical and IO data refers to the data contained by an IO as well as data associated with the IO such as creation time, owner, associated RWEs, when the IO was last accessed, etc. If the IO is a media object, the tern media data can be used. Media data can include any data relating to presentable media, such as audio data, visual data, and audiovisual data. For example, the audio data can be data relating to downloaded music, such as genre, artist, album and the like, and includes data regarding ringtones, ringbacks, media purchased, playlists, and media shared, to name a few. The visual data can be data relating to images and/or text received by the electronic device (e.g., via the Internet or other network). The visual data can be data relating to images and/or text sent from and/or captured at the electronic device. The audiovisual data can be data associated with any videos captured at, downloaded to, or otherwise associated with the electronic device. The media data includes media presented to the user via a network, such as use of the Internet, and includes data relating to text entered and/or received by the user using the network (e.g., search terms), and interaction with the network media, such as click data (e.g., advertisement banner clicks, bookmarks, click patterns and the like). Thus, the media data can include data relating to the user's RSS feeds, subscriptions, group memberships, game services, alerts, and the like. The media data also includes non-network activity, such as image capture and/or video capture using an electronic device, such as a mobile phone. The image data can include metadata added by the user, or other data associated with the image, such as, with respect to photos, location when the photos were taken, direction of the shot, content of the shot, and time of day, to name a few. As described in further detail below, media data can be used, for example, to deduce activities information or preferences information, such as cultural and/or buying preferences information.

The relationship data can include data relating to the relationships of an RWE or IO to another RWE or IO. For example, the relationship data can include user identity data, such as gender, age, race, name, social security number, photographs and other information associated with the user's identity. User identity information can also include e-mail addresses, login names and passwords. Relationship data can further include data identifying explicitly associated RWEs. For example, relationship data for a cell phone can indicate the user that owns the cell phone and the company that provides the service to the phone. As another example, relationship data for a smart car can identify the owner, a credit card associated with the owner for payment of electronic tolls, those users permitted to drive the car and the service station for the car.

Relationship data can also include social network data. Social network data includes data relating to any relationship that is explicitly defined by a user or other RWE, such as data relating to a user's friends, family, co-workers, business relations, and the like. Social network data can include, for example, data corresponding with a user-maintained electronic address book. Relationship data can be correlated with, for example, location data to deduce social network information, such as primary relationships (e.g., user-spouse, user-children and user-parent relationships) or other relationships (e.g., user-friends, user-co-worker, user-business associate relationships). Relationship data also can be utilized to deduce, for example, activities information.

The interaction data can also include transactional data. The transactional data can be any data associated with commercial transactions undertaken by or at the mobile electronic device, such as vendor information, financial institution information (e.g., bank information), financial account information (e.g., credit card information), merchandise information and costs/prices information, and purchase frequency information, to name a few. The transactional data can be utilized, for example, to deduce activities and preferences information. The transactional information can also be used to deduce types of devices and/or services the user owns and/or in which the user can have an interest.

The interaction data can also include device or other RWE interaction data. Such data includes both data generated by interactions between a user and a RWE on the W4 COMN and interactions between the RWE and the W4 COMN. RWE interaction data can be any data relating to an RWE's interaction with the electronic device not included in any of the above categories, such as habitual patterns associated with use of an electronic device data of other modules/applications, such as data regarding which applications are used on an electronic device and how often and when those applications are used. As described in further detail below, device interaction data can be correlated with other data to deduce information regarding user activities and patterns associated therewith. Table 2, below, is a non-exhaustive list including examples of interaction data.

TABLE 2 Examples of Interaction Data Type of Data Example(s) Interpersonal Text-based communications, such as SMS communication data and e-mail Audio-based communications, such as voice calls, voice notes, voice mail Media-based communications, such as multimedia messaging service (MMS) communications Unique identifiers associated with a communication, such as phone numbers, e-mail addresses, and network addresses Media data Audio data, such as music data (artist, genre, track, album, etc.) Visual data, such as any text, images and video data, including Internet data, picture data, podcast data and playlist data Network interaction data, such as click patterns and channel viewing patterns Relationship data User identifying information, such as name, age, gender, race, and social security number Social network data Transactional data Vendors Financial accounts, such as credit cards and banks data Type of merchandise/services purchased Cost of purchases Inventory of purchases Device interaction Any data not captured above dealing with data user interaction of the device, such as patterns of use of the device, applications utilized, and so forth Optimizing the Storage of W4 Data

One aspect of the W4 COMN is the ability to optimize the storage of W4 data by dynamically identifying related communications, even related communications transmitted via different communication channels, and storing all W4 data for those related communications in a single IO. This is an ongoing process of data storage optimization in which all IOs containing W4 data are periodically compared in order to find sufficient commonalities or relationships between the data in the IOs to warrant combining the IOs.

W4 data optimization is a data collection operation performed to reduce the amount of data stored and the number of IOs stored by the W4 COMN as part of its cataloging of communications handled by the network. W4 data optimization is supported by the unique identification of each RWE and by the ability of the W4 COMN to identify relationships between communications that would otherwise be unknown to the operators of specific communication channels.

The W4 data used includes data obtained from device-to-device communications as well as such things as users' social network information and users' electronic calendars.

For the purposes of this disclosure, communication refers to any message of any format that is to be delivered from one RWE to another RWE or an executing process via tile W4 COMN, or via some third party network or communication channel which is detectable by and in communication with the W4 COMN. Thus, a communication can be an email message from one email account to another, a voicemail message left for a computing device such as cell phone, an IM or SMS text message transmitted to a cell phone or computing device, an event request or change transmitted to a list of attendees, or a packet of data transmitted from one software application to another on a different device. A communication can be automatically generated, such as a cellular phone's periodic electronic “handshakes” with nearby cellular towers, or generated by a user operating the RWE or software application. A communication will normally take the form of an IO that is created by one RWE and transmitted to another over the W4 COMN or a third party communication channel. A communication can also be a stream of data, delivery then being considered the opening of the connection with the recipient RWE so that the stream is received.

FIG. 7 illustrates some of the elements in a W4 engine adapted to perform W4 data optimization as described herein. The W4 engine 700 includes a correlation engine 506, an attribution engine 504 and an attention engine 508 as described above. In addition, the W4 engine includes a data optimization engine 702 that, based on the relationships between IOs and RWEs determined by the correlation engine 506 as described below, identifies IOs with at least some redundant data (i.e., one IO contains information that is also included—or determined to be equivalent to information contained—in another IO), and generates a combined IO with some or all the information from the identified IOs. The identified IOs can then be deleted in favor of the combined IO. As described in greater detail below, IOs can be determined to be related or contain redundant information based in part on relationships identified by the correlation engine between RWEs associated with the communications as well as the content of the communications such as a topic, keyword, location and/or time associated with or identified in the communication.

FIG. 8 illustrates an embodiment of a method for optimizing the storage of data related to communications transmitted via different communication channels using social, temporal, spatial and topical data for entities on a network. In the embodiment described below, depending on how the architecture is implemented, the operations described can be performed by one or more of the various engines described above. In addition, sub-engines can be created and used to perform specific operations in order to improve the network's performance as necessary.

As described above, a foundational aspect of the W4 COMN that allows for data optimization is the ongoing collection of W4 data from the RWEs interacting with the network. In an embodiment, this collection is an independent operation 899 of the W4 COMN and thus current W4 social, temporal, spatial and topical data are always available for use in data optimization. In addition, part of this data collection operation 899 includes the determination of ownership and the association of different RWEs with different IOs as described above, as well as the identification of new RWEs when they first interact with the W4 COMN. Therefore, each IO is owned/controlled by at least one RWE with a known, unique identifier on the W4 COMN and each IO can have many other associations with other RWEs that are known to the W4 COMN.

In the data collection operation 899, communications transmitted by different communication channels are monitored by the W4 COMN. In an embodiment, for each communication detected by the W4 COMN, a data IO is generated. This data IO is a record of the pertinent data contained within the communication or related to the communication. The data IO can include a copy of the communication if the communication is itself an information object, however, in an embodiment it is presumed that data is extracted from a communication based on the types of data contained or associated with the communication. An example of the data that can be stored in a data IO includes an identification of a sending device (e.g. the RWE identifier or a channel specific identifier associated with the device), any recipient address associated with the communication, one or more key words or subjects associated with the communication which can be extracted from the content of the communication itself, a time that the communication was transmitted, if the information is available with the communication, a location (e.g. the location of the sending device when the communication was transmitted or, alternatively, a location identifier contained in the content of the communication that can be interpreted by the W4 COMN), or the communication channel upon which the communication is transmitted. Additional data can be stored in the data IO based on the relationships of the data in the communication and other RWEs known to the system. For example, if a sending device associated with the communication is a known proxy for a user or a user's location, then that user can be identified in the data IO as well as the sending device.

The operation 899 can be considered to be an ongoing operation that continuously creates a number of data IOs describing communications over various communication channels handled by the W4 COMN. In an embodiment, these data IOs can be temporarily or permanently stored until they are deleted in a future deletion operation. Such data IOs can also include a communication ID that can be a unique identifier of a communication to the W4 COMN.

In the embodiment shown, the method 800 includes a series of operations that can be continuously or periodically performed. These operations begin with a comparison operation 802 in which the data IOs generated by the data collection operation 899 are compared in order to identify any subset or group of data IOs that contain redundant data as described above. For example, communications that are a record of the electronic handshake between a cell phone and a cell phone tower that indicate that a number of different cell phones were or are at the same or substantially the same location during a specific period of time, can be considered to contain redundant data. The process of determining if two or more data IOs have redundant data can include defining a first time period to be considered redundant, for which any IOs having a time within the first time period are considered to be redundant. This allows two information objects which have times that are close but not exactly the same to still be considered redundant. Determining if two times contained in two different IOs are considered redundant can be performed using any suitable technique. For example, in one embodiment, a plurality of equally spaced windows of time, for example one hour windows of time, can be analyzed. A default condition can be used that any times contained within a predefined period are considered redundant. For example, the time 7:00 am can be on one hand considered to be redundant with 7:45 am because it is within a one hour window, and can also be considered redundant with the time 6:15 because it is within a one hour window of that time also. Such a determination of redundancy can be performed in an iterative process in order to optimize the number of data IOs that are found to contain redundant data.

Likewise, determining if the locations identified in data objects are redundant can also be performed in an iterative fashion by defining a range between the identified locations finding some maximum range between locations for which those locations are considered to be co-located. Such a range can be one mile, one hundred feet, one hundred yards, five meters, or any other definable range. As the comparison operation 802 iterates on the data IOs available, it will ultimately identify a subset of data IOs that contain redundant data. In one embodiment, it is necessary for at least two different data elements within the data IOs to be redundant for a subset of IOs to be created. For example, a location and a time must be redundant between IOs before they are considered to be related. Likewise, users or other RWEs identified in the data IOs need to be the same along with either a location, or a time needs to be identified, in order for two IOs to be considered related and containing redundant information.

For example, in an embodiment, the method 800 in the comparison operation 802 will identify as redundant those communications transmitted from substantially the same location at substantially the same time. Thus, in an event such as a sporting event, a concert, or other gatherings of large groups of people, a large number of communications including cell phone handshakes, telephone calls, email transmissions, credit card transactions, and other communications will be transmitted from, or in some cases to, the gathering of users. Because the data IOs representing each of these communication will be found to be redundant due to the collocation of the communications and the communications happening within a determinable time period, all of these data IOs are the type that could be considered redundant.

After the comparison operation 802 identifies one or more subsets of data IOs that contain redundant information, a generation operation 804 generates a combined information object intended to represent all of the redundant communications in a subset of redundant communications. In an embodiment, the combined IO generated will contain each data element contained in the data IOs of the subset, however the combined IO will eliminate any redundant data, and will represent data determined to be substantially similar as a single data point. For example, times within a given time period can be represented by one representative time. The W4 COMN will understand that the combined IO might not contain the exact time associated with any particular communication, but will be able to determine within a certain time period the “time” of any given communication. By eliminating the redundant data, the combined IO can contain less actual data than the total data contained in the subset of data IOs. In addition, because one combined IO is now stored instead of a plurality of data IOs, searching and identifying IOs will be faster because there are fewer IOs to search, Alternatively, in an embodiment, the combined IO can only contain the actual redundant data and a combined identifier. The data IOs that are considered related and containing redundant information can have the redundant information removed and replaced by the combined information object identifier, thus instead of having two or more data elements in each of the related data IOs, the redundant data elements are replaced by a single data element which identifies the combined IO. While this alternative embodiment creates additional IOs beyond the original number of data IOs, in certain circumstances it can reduce the absolute amount of data stored and thus reduce the data storage requirement for the W4 COMN. The combined IO is then stored on the data backbone of the W4 COMN in a storage operation 806.

In embodiments in which the combined IO contains all of the information contained in each of the related data IOs, a deletion operation 808 can be performed that deletes the related data IOs from the data backbone. This deletion operation then frees up storage space for use in storing data IOs subscribing subsequent data communications.

In order to provide additional detail about how the method 800 described above can be used to identify such things as events or other collections of information as part of the data optimization process, the following examples are provided. Consider, for example, a situation in which a plurality of communication are received from a location such as a sports stadium within a period of several hours. As mentioned above, such communication can be completely different and transmitted over disparate communication channels which are handled by the W4 COMN. For example, credit card transactions of purchases made during the sporting event, tickets sold during the sporting event, and calls made during the sporting event will be included in the communication. In addition, cellular phone handshakes, telephone calls from the sporting events' location, telephone calls made by devices in which the subject of the call is the sporting event which can be determined by the W4 COMN from an inspection of the contents of the call (e.g. using a speech to text system in order to create a text version of phone calls which are then searched for key words which can be associated with the sporting event), instant messages, SMS text and emails transmitted from or to the sporting event containing a discussion or otherwise containing as a subject of the communication the sporting event, as well as photographs taken at the sporting event which can be transmitted or simply detected by the W4 COMN at the time that the photographs were taken, or at a later time, such as when the photographs are saved or transferred to other systems or devices.

In addition, it should be pointed out that some communications might not actually occur during the time of the sporting event, but rather can contain in their content or refer to in their content to the time of the sporting event or an event identifier associated with the sporting event. For example, a user can create on his electronic calendar an event record for the sporting event, for example Chiefs v. Broncos, in which such an event record includes at least a time or time period of the event and some event title associated with the event. Continuing the event record example, other users can be invited to or listed as attendees of the event, and additional information can also be provided in the event record such as location, parking directions, etc. In addition to communications related to the event transmitted prior to the event and during the event, as described above, additional communications can be transmitted related to the above but transmitted after the actual event has occurred. Examples of such communications include the transmission of images taken at the event as determined by the image creation time and potentially any GPS information included in the image or contextual information, or metadata provided with the image, news articles written after the event occurred but referring the subject of the event, and any other communication after the actual occurrence of the event for which it can be determined that the event is related to the communication in some way. Depending on the embodiment, the generation of a combined IO can only combine information objects that are considered actually redundant, as opposed to merely relevant. For example, communications transmitted prior to or after a defined event (i.e. a defined location or area and time period) can be used solely to define the event and those communications need not be combined into the subsequent combined IO, thus the prior and after communications are used in order to provide context to allow a better definition to be developed for the event.

In any case, the comparison operation will identify those IOs containing redundant data and in this case the IOs being data IOs related to communications actually transmitted during the time period of the event and from or to the location of the event. After the comparison operation 802 has identified the subset of IOs based on the event definition created the generation operation 804 then generates an event IO for the event. Data is then copied from each of the subset of related IOs into the event IO forming a complete record of all of the related communications.

In an embodiment, an event IO can include such information as an event ID, an event description discussed in greater detail below, an event time or time period, an event location which can comprise a point and a range from that point. In addition, an event IO can list each device that transmitted a communication and if such devices are known to be proxies for the location of users, can further list each user for which a device is a proxy. The event IO can further contain a list of all of the communication IDs associated with each of the related communication. The communication channel for each communication can also be listed. Additionally, the event IO can further contain any additional data stored in the original data IOs for the communication, thus as shown in the following simple example, list data can be used by creating an event IO that is used in the original data IOs. Consider for example a situation where five hundred cellular phone handshakes are received every second from five hundred different cellular phones at a concert in a park. If, for example, handshakes are received every minute from the cellular phones, an investigation of the history of the location of each cellular phone will reveal that each of the five hundred cellular phones arrived at the concert location at various times and from various locations and directions, however at some point all or substantially all of the five hundred cellular phone handshake communications will indicate that those devices were at the location of the concert within a time period ultimately defined to be the time period for the concert. If the concert is one hundred minutes long and five hundred handshakes are received every minute, this will result in fifty thousand data IOs being created in order to document each one of the handshake communications. The comparison operation will identify these fifty thousand data IOs as being related based on an analysis of their common data (e.g. time and location). Furthermore, if the cellular phones are known to be proxies for the location of different users, the users associated with each cellular phone can further be identified. In this example then, the generation operation 804 will generate an event IO for the concert. The event IO will contain an event ID and possibly an event title or description as well as the time period during which the concert occurred. This time period can be determined based on prior event information obtained from an electronic calendar, or alternatively be inferred from the data contained in the data IOs, and also contain the location of the concert. The event IO can then contain a listing of each of the cellular phone devices that were detected at the concert and can further identify the users (RWEs) associated with those phones. In an embodiment, an arrival time and departure time can further be identified for each one of the devices detected at the concert. Accordingly, by creating a single event IO containing the time, a location, and a listing of the five hundred devices at the concert, even if additional user information and departure and arrival time are also stored, it is clear that the resulting amount of data stored in the event IO is substantially less than the amount of data required to store the fifty thousand data IOs, even if those data IOs contained only the information of time, location, and device.

The example above further illustrates how the data optimization system can dynamically identify events which can or can not be recognized by any individual communication system or indeed by any actual user or process. Another example of this dynamic identification of events which might not be recognized by other users is an unpredictable event, such as a traffic jam on a highway. In this example, consider that some number of travelers independently driving on a stretch of a highway are halted due to an accident or other occurrence. As the number of cars and drivers and passengers in those cars comes to a halt and collect at the point of the traffic jam, the W4 COMN, by collecting handshakes from cell phones, transmissions from mobile devices including any smart vehicles or other devices such as, for example, requests on the network for traffic information, or 911 calls related to an accident, or indeed simple telephone calls by people who no longer need to pay attention to driving because of the traffic jam. Other communications unrelated to the users such as traffic sensors on the highway can also be sending communications which can be detected by the W4 COMN and determined to have related information. In this case, such redundant data will include the location corresponding to the highway at which the traffic jam is occurring and the time of the traffic jam. As time progresses and the traffic jam does not immediately resolve itself, at some point a threshold number of data information objects will be created from the communications generated by the people and devices in the traffic jam, and the data optimization engine will identify the traffic jam as an event. By correlating knowledge of the location, for example, various portions of highways and roads can be identified on a W4 COMN as different independent RWEs. The fact that the event is a traffic jam or some other accident or impairment of vehicle flow can be automatically determined. In an embodiment, such a dynamic identification of a traffic jam could generate a notification or alert to local law enforcement and emergency services as well as tow services or other services which can be useful in resolving the traffic jam. Yet another use of this aspect of dynamically identifying an event from disparate communications is the automatic generation of notifications or alerts based on the detection of an event. For example, if a traffic jam is detected and is further determined that a specific user is stuck in the traffic jam, a notification can automatically be transmitted to any approaching meetings for which that user is an attendee, or to other RWEs in the vicinity to avoid the road impacted by the traffic jam. In this way, if a user is stuck in a traffic jam the attendees of the upcoming meeting will be atomically notified that the user will be late to the meeting and can adjust their schedules or the prosecution of the meeting in light of this information.

When defining an event as described with reference to the comparison operation 802 and the generation operation 804, additional communications either before or after the actual event can be used in order to define the event itself. When defining the event, such prior or post communications and data related to those communications can be used to generate an event description for the event. For example, if there is a scheduled sporting event it can occur as a scheduled meeting on some number of electronic calendars of people who intend to go to the sporting event. Based on the W4 COMN's analysis of these electronic event records, commonalities can be determined between the title of the event. For example, a number of the event records can be title “football game”, while other event records can be titled “Chiefs v. Broncos”, while other event records can be titled “big game at mile high”. All of these titles can be analyzed by the W4 COMN in order to generate some event description for the event.

When generating an event description, the data optimization engine can, where possible, used default event titles from some master listing of scheduled events such as a sports calendar of events or concert calendar of events that is designated as a preference over user generated names on user calendars. Such electronic event records and preferred listings of events can also be used in order to define the event time period for the event. Alternatively, the event time period can be determined based on a bell curve analysis or some other statistical analysis of the number of users at the location at any given time. For example, an event can be defined in absolute terms as any collection of more that a threshold number of users for a time period longer than some designated amount of time (e.g. five minutes or thirty minutes). Event descriptions can also be generated from information known about the location of the event, as described above with reference to this traffic jam example. If the event location is determined to be a highway, then the event can be referred to as a traffic jam absent any other identifiable information related to the event.

Alternatively, if the event location is a sports arena, the event can be generically identified as an event at that sports arena, absent any other information indicating what type of event (although W4 COMN can likely deduce the type of event from other available information such as ticket sale information or RWE calendar entries).

Other methods of defining events can also be used such as, for example, defining an event as a minimum number of users within a first defined area for a minimum amount of time, such as two consecutive predefined time periods or time windows. In addition to dynamically identifying events from the data IOs, the methods and systems described here for data optimization can also be used to dynamically identify locations or other RWEs that have the same status or condition during a given time period. For example, take a set of communications generated by a group of rainfall gauges located throughout a region. In this example, each rainfall gauge will have an identifier which is associated with the location or can in fact include a location. Rainfall gauges can periodically transmit information indicating the amount of rainfall detected in that period of time, so, for example, if it is raining in one portion of the city and not in another, then the rainfall gauges throughout the city will periodically report the rainfall statistics.

The data optimization method described herein can be used to take the different data IOs generated for each communication from each rainfall gauge and combine those data IOs into some number of combined status or condition IOs. In this example, a condition IO can include an identifier of some condition (e.g. rain detected at this location) as well as identification of a time period and a listing of the locations or rainfall gauges which detected the rain during that time period. A second condition IO can further be created in which the condition of not raining is identified along with the time period and all of the rainfall gauges are identified for which it was not raining during that time period. Such condition IOs can be categorized in different ways based on the ability of the sensors to detect conditions. For example, if the rainfall gauges are designed to identify rainfall in half inch of precipitation increments, then separate condition IOs can be created for each incremental amount of rainfall detected (e.g. a condition IO for no rainfall detected, a condition IO for the range 0.01 inches of rainfall to 0.5 inches of rainfall, a condition IO for 0.051 inches of rainfall to 1.0 inches of rainfall, etc.).

A condition that can be identified by the W4 COMN, and for which data is stored in data IOs, can be used as a data element for which related data IOs are determined. As another example, delays in air traffic at specific airports can be a condition upon which data IOs are combined into a combined IO. In this example separate communications from each airport in a region or country can be received from data IOs generated for each airport indicating the airport and whether air traffic delays are occurring or not at that time. These data IOs when compare can be determined to be related because they have a related condition (e.g. no traffic delays or known traffic delays) and a time that are redundant, thus all of the data IOs can he combined into one of two condition IOs with a first condition IO being the no air traffic delays IO for a specific time period, that IO listing all of the airports that for the time period have no air traffic delays, and a second IO being the condition IO of airport with traffic delays, and again specifying a time period and the listing of airports reporting air traffic delays. In this way the plurality of communications associated with air traffic delays from each airport can be combined into two condition IOs for any given time or time period.

Those skilled in the art will recognize that the methods and systems of the present disclosure can be implemented in many manners and as such are not to be limited by the foregoing exemplary embodiments and examples. In other words, functional elements being performed by single or multiple components, in various combinations of hardware and software or firmware, and individual functions, can be distributed among software applications at either the client level or server level or both. In this regard, any number of the features of the different embodiments described herein can be combined into single or multiple embodiments, and alternate embodiments having fewer than, or more than, all of the features described herein are possible. Functionality can also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software/hardware/firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, as well as those variations and modifications that can be made to the hardware or software or firmware components described herein as would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described as flowcharts in this disclosure are provided by way of example in order to provide a more complete understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of the various operations is altered and in which sub-operations described as being part of a larger operation are performed independently.

While various embodiments have been described for purposes of this disclosure, such embodiments should not be deemed to limit the teaching of this disclosure to those embodiments. Various changes and modifications can be made to the elements and operations described above to obtain a result that remains within the scope of the systems and processes described in this disclosure. For example, the data optimization system may further be used to efficiently store data related to tracking objects by RFID. By treating each detection of an different object at a location and within a time period as a separate communication, the data describing the location of the objects can be efficiently stored by combining separate data IOs for each detection into a combined IO listing all objects detected at a sensor within a time period. Numerous other changes can be made that will readily suggest themselves to those skilled in the art and which are encompassed in the spirit of the disclosure and as defined in the appended claims. 

What is claimed is:
 1. A method for storing information objects (IOs) comprising: generating, via at least one processor of a computing device, a plurality of user-related first IOs, each user-related first IO containing data describing a communication transmitted via one of a plurality of communication channels, the data in each user-related first IO identifying a user that is capable of communicating via at least one of the plurality of communication channels and being at a location at a time; temporarily storing the plurality of user-related first IOs in a computer-readable storage medium in communication with the computing device; analyzing, via at least one processor of the computing device, the plurality of user-related first IOs to identify a subset of user-related first IOs comprising data identifying locations of users within a selected distance from a first location and a determined time within a first time period; generating, via at least one processor of the computing device, an event IO comprising data identifying the locations within the selected distance from the first location, the determined time within the first time period and each of the users identified in the subset of user-related first IOs; storing the event IO in a datastore in communication with said computing device; and deleting, via at least one processor of the computing device, the subset of user-related first IOs.
 2. The method of claim 1 further comprising: defining the first time period based upon a number of users within the determined time at the first location.
 3. The method of claim 1 further comprising: defining the first distance based on a number of users within the selected distance from the first location.
 4. The method of claim 1 further comprising: storing, in the event IO, any additional data stored in each of the subset of user-related first IOs.
 5. The method of claim 1 further comprising: generating an event description for the event IO based on information contained in the subset of user-related first IOs.
 6. The method of claim 5 further comprising: generating a plurality of user-related second IOs, each user-related second IO containing data describing a communication transmitted via one of a plurality of communication channels, the data in each user-related second IO containing a future event definition containing an event time and an event title; searching the plurality of user-related second IOs for future event definitions with an event time within the first time period; and generating the event description based on titles of future event definitions having an event time within the first time period.
 7. The method of claim 6 further comprising: selecting one of the event titles from the future event definitions having an event time within the first time period.
 8. The method of claim 5 further comprising: generating the event description based on the first location.
 9. The method of claim 2 further comprising: generating the event IO if the number of users within the selected distance from the first location is greater than a predetermined threshold.
 10. The method of claim 1 further comprising: for at least one communication detected, identifying a sending device that transmitted the communication, a transmission time the transmission was sent and a device location of the sending device at the time the transmission was sent; determining that the sending device is a proxy for a location of a first user; and generating a user-related first IO identifying the first user being at the device location at the transmission time.
 11. A method for storing information objects (IOs) comprising: monitoring a plurality of communications transmitted via one of a plurality of communication channels on a network; identifying, via a computing device, a subset of related communications based on an analysis of the content of the plurality of communications; generating, via the computing device, a user-related information object (IO) from the subset of related communications, the user-related IO containing data from the plurality of communications; and storing the user-related IO as a record of the subset of related communications in a datastore in communication with the computing device.
 12. The method of claim 11 further comprising: retrieving non-redundant data obtained from each of the subset of related communications from the user-related IO; and storing the non-redundant data.
 13. The method of claim 11 further comprising: determining that the subset of related communications contains content indicating that a plurality of users were co-located during a specified time period; and generating an event IO containing the content from the subset of related communications, wherein the event IO identifies the plurality of users, an event location and the specified time period.
 14. The method of claim 11 further comprising: determining that the subset of related communications contains additional content indicating that a plurality of users were performing a specified activity during the specified time period; generating an activity IO containing the additional content of the subset of related communications, the activity IO identifies the specified activity, the plurality of users and the specified time period.
 15. The method of claim 11 further comprising: determining that the subset of related communications contain information indicating that a plurality of locations were associated with a specified condition during the specified time period; generating a condition IO containing the information from the subset of related communications, the condition IO identifying the specified condition, the plurality of locations and the specified time period.
 16. The method of claim 12 further comprising: storing a communication identifier in the user-related IO for each communication in the subset of related communications and the associated communication channel.
 17. A system for optimizing the storage of user-related information objects (IOs) comprising: a plurality of processors; a correlation engine, implemented by at least one of said plurality of processors, connected via at least one communication channel to a plurality of computing devices, that identifies relationships between the user-related IOs and associated real-world entities (RWEs) including the plurality of computing devices; storage connected to the correlation engine that stores at least one of social data, spatial data, temporal data and logical data associated with the RWEs; and a data optimization engine implemented by at least one of said plurality of processors that determines redundant data contained with the user-related IOs based upon the correlated relationships between the user-related IOs and the RWEs identified by the correlation engine, and generates a combined user-related IO with at least a portion of the user-related IOs.
 18. The system of claim 17, wherein the combined user-related IO is stored on a data backbone of a network through which the at least one communication channel communicates.
 19. The system of claim 18, wherein the redundant data with the user-related IOs are deleted upon the combined user-related IO accumulating all of the data. 